Data clarity in the Murray-Darling
Geoscience Australia, Australian Capital Territory
A detailed understanding of vegetation and flooding scenarios in the vast Murray-Darling basin is of prime economic, social and environmental significance to Australia.
To enable accurate flood models to be developed, Geoscience Australia commissioned the capture of terabytes of high-resolution point cloud and associated waveform LiDAR data across the basin over many months.
However, during post-processing the link between the waveform files and the point cloud files was inadvertently lost, rendering the waveform data useless. The whole exercise was placed in jeopardy.
Anditi’s spatial data experts were given a formidable challenge: re-establish the unique connection between 120 billion waveforms from 55,000 km^2 of data capture to the massive point cloud dataset from the same area.
By brute force solving such a puzzle would take centuries. The Anditi team worked from first principles to develop a series of complex algorithms that allowed each point in the point cloud to be connected to the corresponding waveform, thereby correctly positioning it in 3D space.
Multiple corner cases and data quality issues were overcome. Finally, the computing power of the National Computational Infrastructure was brought to bear to re-create a fully viable, coherent dataset.
- Waveform LiDAR and point cloud
- First principles
- Data know-how
- Precise algorithms
- Scalable computing
The cutting-edge solution developed by the Anditi team allowed 98% of the previously useless data to be successfully saved. Geoscience Australia’s multi-million dollar investment in data capture could be salvaged.
Thanks to Anditi’s deep and detailed understanding of spatial data, one of the biggest public waveform LiDAR datasets in the world could be used to its fullest, enabling a better understanding of flooding and vegetation across a crucial region of Australia.
Value of data secured
Avoiding massive waste of time and money
Maximised return on investment
Smart algorithms enabling cost-efficient data correction
Critical environment understood
Project aims achieved with input data fit-for-purpose